National Survey of Wellness Programs in U.S. and Canadian Medical Schools
Bibliographic record
Abstract
PURPOSE: To describe the prevalence and scope of wellness programs at U.S. and Canadian medical schools. METHOD: In July 2019, the authors surveyed 159 U.S. and Canadian medical schools regarding the prevalence, structure, and scope of their wellness programs. They inquired about the scope of programming, mental health initiatives, and evaluation strategies. RESULTS: Of the 159 schools, 104 responded (65%). Ninety schools (93%, 90/97) had a formal wellness program, and across 75 schools, the mean full-time equivalent (FTE) support for leadership was 0.77 (standard deviation [SD] 0.76). The wellness budget did not correlate with school type or size (respectively, P = .24 and P = .88). Most schools reported adequate preventative programming (62%, 53/85), reactive programming (86%, 73/85), and cultural programming (52%, 44/85), but most reported too little focus on structural programming (56%, 48/85). The most commonly reported barrier was lack of financial support (52%, 45/86), followed by lack of administrative support (35%, 30/86). Most schools (65%, 55/84) reported in-house mental health professionals with dedicated time to see medical students; across 43 schools, overall mean FTE for mental health professions was 1.62 (SD 1.41) and mean FTE per student enrolled was 0.0024 (SD 0.0019). Most schools (62%, 52/84) evaluated their wellness programs; they used the Association of American Medical Colleges Graduation Questionnaire (83%, 43/52) and/or annual student surveys (62%, 32/52). The most commonly reported barrier to evaluation was lack of time (54%, 45/84), followed by lack of administrative support (43%, 36/84). CONCLUSIONS: Wellness programs are widely established at U.S. and Canadian medical schools, and most focus on preventative and reactive programming, as opposed to structural programming. Rigorous evaluation of the effectiveness of programs on student well-being is needed to inform resource allocation and program development. Schools should ensure adequate financial and administrative support to promote students' well-being and success.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".